Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x262a6ac7cc0>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x262a6b7dda0>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.4.0
Default GPU Device: /device:GPU:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_real = tf.placeholder(tf.float32, shape=(None,image_width,image_height,image_channels),name='input_read')
    input_z = tf.placeholder(tf.float32,shape=(None,z_dim),name='input_z')
    input_lr = tf.placeholder(tf.float32,name="learning_rate")
    

    return input_real,input_z,input_lr


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    alpha = .2
    kernel_fs = 4
    keep_probability = .9 # review recommendation
    with tf.variable_scope('discriminator', reuse=reuse):
        #print(images)
        h1 = tf.layers.conv2d(images, 128,  kernel_fs, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer(), activation = None)
        do1 = tf.nn.dropout(h1,keep_probability)
        relu1 = tf.maximum(alpha * do1, do1)
        #print(relu1)
        
        h2 = tf.layers.conv2d(relu1, 256,  kernel_fs, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer(), activation = None)
        bn2 = tf.layers.batch_normalization(h2, training=True)
        do2 = tf.nn.dropout(bn2, keep_probability)  # review recommendation
        relu2 = tf.maximum(alpha * do2, do2)
        #print(relu2)
        
        h3 = tf.layers.conv2d(relu2, 512, kernel_fs, strides=2, padding='same',kernel_initializer=tf.contrib.layers.xavier_initializer(), activation = None)
        bn3 = tf.layers.batch_normalization(h3, training=True)
        #do3 = tf.nn.dropout(bn3, keep_probability)  # review recommendation
        relu3 = tf.maximum(alpha * bn3, bn3)
    
        tf_size = relu3.get_shape().as_list()
        fl_size = None
        if len(tf_size)>1 :
            fl_size = tf_size[1]*tf_size[2]*tf_size[3]
        else:
            fl_size = 4*4*256
        
        #print(fl_size)
        
        # Flatten it
        flat = tf.reshape(relu3, (-1, fl_size))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        return out, logits

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
import numpy as np
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    alpha = .2
    reuse = not is_train
    kernel_fs = 4
    outImageSize = 28 # Need to generate 28 x 28 image; 
    start_size = np.int32(outImageSize/(2*2) - 1) #upsampling twice with strides of 2; subtract 1 and use stride of 1 and valid padding to get 7 x 7 image
    keep_probability = 0.8 # for dropout  # review recommendation
    #print(start_size)
    with tf.variable_scope('generator', reuse=reuse):
        # First fully connected layer
        #print(z)
        #print(out_channel_dim)
        h1 = tf.layers.dense(z,   start_size*  start_size*512, activation = None)
       
        # Reshape it to start the convolutional stack
        h1 = tf.reshape(h1, (-1,   start_size,   start_size, 512))
        h1 = tf.layers.batch_normalization(h1, training=is_train)
        h1 = tf.nn.dropout(h1, keep_probability)  # review recommendation
        h1 = tf.maximum(alpha * h1, h1)
        #print(h1)
        
        h2 = tf.layers.conv2d_transpose(h1, 256, 2, strides=1, padding='valid', kernel_initializer=tf.contrib.layers.xavier_initializer(), activation = None)
        h2 = tf.layers.batch_normalization(h2, training=is_train)
        h2 = tf.nn.dropout(h2, keep_probability)  # review recommendation
        h2 = tf.maximum(alpha * h2, h2)
        #print(h2)
        
        h3 = tf.layers.conv2d_transpose(h2, 128, kernel_fs, strides=2, padding='same',kernel_initializer=tf.contrib.layers.xavier_initializer(), activation = None)
        h3 = tf.layers.batch_normalization(h3,training=is_train)
        #h3 = tf.nn.dropout(h3, keep_probability)
        h3 = tf.maximum(alpha * h3, h3)
        #print(h3)
       # Output layer
        logits = tf.layers.conv2d_transpose(h3,out_channel_dim, kernel_fs, strides=2, padding='same',kernel_initializer=tf.contrib.layers.xavier_initializer(), activation = None)
        #print(logits)
        
        out = tf.nn.tanh(logits)
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    fake_model = generator(input_z, out_channel_dim, is_train=True)
    d_model_real, d_logits_real = discriminator(input_real, reuse=False) 
    d_model_fake, d_logits_fake = discriminator(fake_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)*.9))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    # Get weights and bias to update
    trainable_vars = tf.trainable_variables()
    d_vars = [var for var in trainable_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in trainable_vars if var.name.startswith('generator')]
    
    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
    
    
    return d_train_opt, g_train_opt

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, lr, beta1)
    steps = 0 
    print_every=100
    show_every=100
    #samples, losses = [], []
    saver = tf.train.Saver()

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps +=1
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                batch_images = batch_images*2; # Convert the -.5 to .5 range in -1 to 1.
                # Run optimizers
                _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr:learning_rate})
                _ = sess.run(g_train_opt, feed_dict={input_z: batch_z, input_real: batch_images,lr:learning_rate})

                # At the end of each epoch, get the losses and print them out
                train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                train_loss_g = g_loss.eval({input_z: batch_z})
                reloop=0
                while ((train_loss_g > train_loss_d) | (train_loss_g > 1)) & (reloop < 5): # continue updating for the batch until the condition are satisfied
                        _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr:learning_rate})
                        _ = sess.run(g_train_opt, feed_dict={input_z: batch_z, input_real: batch_images,lr:learning_rate})
                        train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                        train_loss_g = g_loss.eval({input_z: batch_z})
                        reloop+=1
                 
                if (steps % print_every == 0 or steps==1):
                    print("Epoch {}/{}...".format( epoch_i+1, epochs),"...Steps {}...".format( steps),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                  

                if (steps % show_every == 0 or steps==1):
                    show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)

        print("Done")
        show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)
    return  True #losses, samples           

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [12]:
batch_size = 100
z_dim = 100
learning_rate = 0.0005 #
beta1 = 0.2 # 
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... ...Steps 1... Discriminator Loss: 5.1215... Generator Loss: 0.0599
Epoch 1/2... ...Steps 100... Discriminator Loss: 1.3371... Generator Loss: 0.5341
Epoch 1/2... ...Steps 200... Discriminator Loss: 1.3925... Generator Loss: 0.5599
Epoch 1/2... ...Steps 300... Discriminator Loss: 1.6810... Generator Loss: 0.3798
Epoch 1/2... ...Steps 400... Discriminator Loss: 1.5551... Generator Loss: 0.4705
Epoch 1/2... ...Steps 500... Discriminator Loss: 1.5294... Generator Loss: 0.4609
Epoch 1/2... ...Steps 600... Discriminator Loss: 1.2254... Generator Loss: 0.7649
Epoch 2/2... ...Steps 700... Discriminator Loss: 1.0990... Generator Loss: 0.8754
Epoch 2/2... ...Steps 800... Discriminator Loss: 1.6137... Generator Loss: 0.5096
Epoch 2/2... ...Steps 900... Discriminator Loss: 1.6357... Generator Loss: 0.5190
Epoch 2/2... ...Steps 1000... Discriminator Loss: 1.2023... Generator Loss: 0.7220
Epoch 2/2... ...Steps 1100... Discriminator Loss: 0.7546... Generator Loss: 3.4606
Epoch 2/2... ...Steps 1200... Discriminator Loss: 0.5712... Generator Loss: 2.2718
Done

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [13]:
batch_size =32 #Increased batch size per review
z_dim =100
learning_rate = 0.0005# increased as per the review, 
beta1 = .2

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... ...Steps 1... Discriminator Loss: 3.4462... Generator Loss: 0.1838
Epoch 1/1... ...Steps 100... Discriminator Loss: 1.3858... Generator Loss: 0.7173
Epoch 1/1... ...Steps 200... Discriminator Loss: 1.6148... Generator Loss: 0.5801
Epoch 1/1... ...Steps 300... Discriminator Loss: 1.2451... Generator Loss: 0.7716
Epoch 1/1... ...Steps 400... Discriminator Loss: 1.3719... Generator Loss: 0.6383
Epoch 1/1... ...Steps 500... Discriminator Loss: 1.1223... Generator Loss: 0.9337
Epoch 1/1... ...Steps 600... Discriminator Loss: 1.1904... Generator Loss: 0.8722
Epoch 1/1... ...Steps 700... Discriminator Loss: 1.3645... Generator Loss: 0.5545
Epoch 1/1... ...Steps 800... Discriminator Loss: 2.1323... Generator Loss: 0.2945
Epoch 1/1... ...Steps 900... Discriminator Loss: 1.8480... Generator Loss: 0.3350
Epoch 1/1... ...Steps 1000... Discriminator Loss: 1.2564... Generator Loss: 0.7281
Epoch 1/1... ...Steps 1100... Discriminator Loss: 0.9883... Generator Loss: 0.8582
Epoch 1/1... ...Steps 1200... Discriminator Loss: 0.7707... Generator Loss: 1.0371
Epoch 1/1... ...Steps 1300... Discriminator Loss: 1.0476... Generator Loss: 0.8329
Epoch 1/1... ...Steps 1400... Discriminator Loss: 0.4537... Generator Loss: 3.2040
Epoch 1/1... ...Steps 1500... Discriminator Loss: 0.4686... Generator Loss: 2.2251
Epoch 1/1... ...Steps 1600... Discriminator Loss: 1.0937... Generator Loss: 0.9560
Epoch 1/1... ...Steps 1700... Discriminator Loss: 0.5141... Generator Loss: 2.4708
Epoch 1/1... ...Steps 1800... Discriminator Loss: 0.5692... Generator Loss: 1.8079
Epoch 1/1... ...Steps 1900... Discriminator Loss: 0.4050... Generator Loss: 3.2652
Epoch 1/1... ...Steps 2000... Discriminator Loss: 0.4325... Generator Loss: 2.9372
Epoch 1/1... ...Steps 2100... Discriminator Loss: 1.5295... Generator Loss: 0.6877
Epoch 1/1... ...Steps 2200... Discriminator Loss: 0.6944... Generator Loss: 2.3199
Epoch 1/1... ...Steps 2300... Discriminator Loss: 0.5539... Generator Loss: 2.0241
Epoch 1/1... ...Steps 2400... Discriminator Loss: 0.4304... Generator Loss: 3.4705
Epoch 1/1... ...Steps 2500... Discriminator Loss: 0.4401... Generator Loss: 4.5688
Epoch 1/1... ...Steps 2600... Discriminator Loss: 0.4174... Generator Loss: 4.2508
Epoch 1/1... ...Steps 2700... Discriminator Loss: 0.7571... Generator Loss: 1.8780
Epoch 1/1... ...Steps 2800... Discriminator Loss: 0.5522... Generator Loss: 2.3062
Epoch 1/1... ...Steps 2900... Discriminator Loss: 0.4184... Generator Loss: 3.3594
Epoch 1/1... ...Steps 3000... Discriminator Loss: 0.5163... Generator Loss: 2.6618
Epoch 1/1... ...Steps 3100... Discriminator Loss: 0.3699... Generator Loss: 4.0523
Epoch 1/1... ...Steps 3200... Discriminator Loss: 0.4213... Generator Loss: 3.1921
Epoch 1/1... ...Steps 3300... Discriminator Loss: 0.3408... Generator Loss: 4.6656
Epoch 1/1... ...Steps 3400... Discriminator Loss: 1.1928... Generator Loss: 0.7778
Epoch 1/1... ...Steps 3500... Discriminator Loss: 1.1855... Generator Loss: 0.7446
Epoch 1/1... ...Steps 3600... Discriminator Loss: 0.4076... Generator Loss: 4.0155
Epoch 1/1... ...Steps 3700... Discriminator Loss: 0.4221... Generator Loss: 3.1917
Epoch 1/1... ...Steps 3800... Discriminator Loss: 0.4099... Generator Loss: 3.4807
Epoch 1/1... ...Steps 3900... Discriminator Loss: 0.4337... Generator Loss: 2.7249
Epoch 1/1... ...Steps 4000... Discriminator Loss: 0.3657... Generator Loss: 4.0178
Epoch 1/1... ...Steps 4100... Discriminator Loss: 0.3741... Generator Loss: 3.0658
Epoch 1/1... ...Steps 4200... Discriminator Loss: 0.4561... Generator Loss: 3.2933
Epoch 1/1... ...Steps 4300... Discriminator Loss: 0.4126... Generator Loss: 4.0465
Epoch 1/1... ...Steps 4400... Discriminator Loss: 0.4002... Generator Loss: 3.0208
Epoch 1/1... ...Steps 4500... Discriminator Loss: 0.4086... Generator Loss: 4.4265
Epoch 1/1... ...Steps 4600... Discriminator Loss: 0.4173... Generator Loss: 2.3131
Epoch 1/1... ...Steps 4700... Discriminator Loss: 0.3688... Generator Loss: 3.8877
Epoch 1/1... ...Steps 4800... Discriminator Loss: 0.3853... Generator Loss: 3.5186
Epoch 1/1... ...Steps 4900... Discriminator Loss: 0.4201... Generator Loss: 2.6109
Epoch 1/1... ...Steps 5000... Discriminator Loss: 0.3596... Generator Loss: 3.7721
Epoch 1/1... ...Steps 5100... Discriminator Loss: 0.4019... Generator Loss: 2.9761
Epoch 1/1... ...Steps 5200... Discriminator Loss: 0.3889... Generator Loss: 5.4444
Epoch 1/1... ...Steps 5300... Discriminator Loss: 0.4226... Generator Loss: 3.7102
Epoch 1/1... ...Steps 5400... Discriminator Loss: 0.3664... Generator Loss: 3.6834
Epoch 1/1... ...Steps 5500... Discriminator Loss: 0.4108... Generator Loss: 3.0266
Epoch 1/1... ...Steps 5600... Discriminator Loss: 0.3832... Generator Loss: 3.8237
Epoch 1/1... ...Steps 5700... Discriminator Loss: 0.4615... Generator Loss: 3.1717
Epoch 1/1... ...Steps 5800... Discriminator Loss: 0.3582... Generator Loss: 5.0204
Epoch 1/1... ...Steps 5900... Discriminator Loss: 0.3516... Generator Loss: 5.5217
Epoch 1/1... ...Steps 6000... Discriminator Loss: 0.3779... Generator Loss: 3.2843
Epoch 1/1... ...Steps 6100... Discriminator Loss: 0.4201... Generator Loss: 3.0180
Epoch 1/1... ...Steps 6200... Discriminator Loss: 0.3614... Generator Loss: 4.2058
Epoch 1/1... ...Steps 6300... Discriminator Loss: 0.4114... Generator Loss: 3.2931
Done

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.

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